报 告 人：徐茂超 教授
报告题目：Ensemble Bivariate Copulas for Modeling Multivariate Cyber Data Breach Risks with Insurance Applications
报告时间：2023年06月04日（周日）下午3：30
报告地点：静远楼1506会议室
主办单位：数学与统计学院、数学研究院、科学技术研究院
报告人简介：
徐茂超，现为伊利诺伊州立大学数学系终身教授，研究生项目Director，网络风险保险专家。徐教授曾多次担任网络风险保险的学术顾问，例如美国网络风险评估公司Safe Security的顾问，以及新加坡虚拟货币保险公司InsurAce的顾问。徐教授的研究领域为网络风险评估，统计建模以及网络风险保险。他的研究曾获伊利诺伊州立大学杰出研究奖，北美精算师协会最佳论文奖，以及应用统计杂志最佳论文推荐奖等。近年来相关研究的成果均发表于Annals of Applied Statistics, Technometrics，IEEE Transactions on Information Forensics and Security，IISE Transactions 等国际顶级期刊。
报告摘要：
Modeling the multivariate dependence among cyber data breach risks presents a significant challenge due to the sparsity and heavy tail properties exhibited by breach events. In this talk, we introduce a novel ensemble learning approach that effectively captures both the temporal and cross-sectional dependence inherent in cyber risks. Our approach leverages bivariate copulas to generate predictive members, and the resulting predictive distribution is carefully calibrated by minimizing the distribution score. Moreover, we demonstrate the applicability of our proposed model in the domain of insurance pricing. Through extensive simulations and analysis of real-world data, our findings reveal that our approach outperforms existing methodologies reported in the literature. The superior performance of our approach highlights its potential to enhance risk assessment and insurance pricing practices related to cyber data breaches.